Additional compression for existing compressed data

ABSTRACT

Techniques are provided for implementing additional compression for existing compressed data. Format information stored within a data block is evaluated to determine whether the data block is compressed or uncompressed. In response to the data block being compressed according to a first compression format, the data block is decompressed using the format information. The data block is compressed with one or more other data blocks to create compressed data having a second compression format different than the first compression format.

RELATED APPLICATIONS

This application claims priority to India Patent Application, titled“ADDITIONAL COMPRESSION FOR EXISTING COMPRESSED DATA”, filed on Jun. 26,2020 and accorded Indian Application No.: 202041027165, which isincorporated herein by reference.

BACKGROUND

Many storage environments provide storage efficiency functionality fordata stored on behalf of clients within the storage environments. Forexample a node of a storage environment (e.g., a server, a virtualmachine, hardware, software, or combination thereof) may providecompression functionality for user data. The node may compress the userdata so that less storage is consumed. In another example, the node mayprovide deduplication functionality for the user data. The node maydeduplicate the user data in order to identify and eliminate redundantlystored data. In this way, the node may implement various types ofstorage efficiency functionality in order to more efficiently store dataon behalf of clients.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example computing environmentin which an embodiment of the invention may be implemented.

FIG. 2 is a block diagram illustrating a network environment withexemplary node computing devices.

FIG. 3 is a block diagram illustrating an exemplary node computingdevice.

FIG. 4 is a flow chart illustrating an example method for implementingadditional compression for existing compressed data.

FIG. 5A is a block diagram illustrating an example system forimplementing additional compression for existing compressed data.

FIG. 5B is a block diagram illustrating an example system forimplementing additional compression for existing compressed data, wherecompressed data (D) having a first compression format is written tostorage.

FIG. 5C is a block diagram illustrating an example system forimplementing additional compression for existing compressed data, wherecompressed data (D) is recompressed according to a second compressionformat.

FIG. 5D is a block diagram illustrating an example system forimplementing additional compression for existing compressed data, whererecompressed data (D) is requested.

FIG. 6 is an example of a computer readable medium in which anembodiment of the invention may be implemented.

DETAILED DESCRIPTION

Some examples of the claimed subject matter are now described withreference to the drawings, where like reference numerals are generallyused to refer to like elements throughout. In the following description,for purposes of explanation, numerous specific details are set forth inorder to provide an understanding of the claimed subject matter. It maybe evident, however, that the claimed subject matter may be practicedwithout these specific details. Nothing in this detailed description isadmitted as prior art.

The techniques described herein are directed to providing additionalcompression for existing compressed data in order to achieve additionalstorage compression savings to reduce storage consumption. Inparticular, compressed data may have been compressed at an applicationor file level with a relatively lighter-weight compression algorithmthat provides some storage compression savings. As provided herein,format information describing how to decompress the compressed data isused to decompress the compressed data, which is then recompressed at astorage layer or container layer level with one or more additional datablocks using a relatively heavier-weight compression algorithm and/orlarger compression size. The relatively heavier-weight compressionalgorithm provides more storage compression savings than the relativelylighter-weight compression algorithm. In this way, storage consumptionis reduced.

In an example, a node, such as a server, a computing device, a virtualmachine, a storage service, hardware, software, or combination thereof,may store data on behalf of client devices. The node may also providevarious types of storage efficiency functionality, such asdeduplication, compression, etc. As provided herein, the node mayprovide additional compression for existing compressed data. That is,when data is written to a file within storage, the data may becompressed. In an example, the data may be compressed by an applicationaccessing the file because the data is written to the file. In anotherexample, the data may be compressed at a file level by combining somefile blocks of the file (e.g., compression after the data is written tofile blocks of the file on storage).

If an original compression format of the compressed data is known, suchas by a file system or other functionality of the node, then additionalcompression storage savings can be achieved, as provided herein. Theadditional compression storage savings can be achieved by decompressingcompressed data blocks as uncompressed data blocks. The uncompresseddata blocks are then recompressed with one or more additional datablocks using a different compression format. For example, theuncompressed data blocks may be recompressed using a larger chunk size(e.g., a larger compression size encompassing the uncompressed datablocks along with the additional data blocks) and/or by using aheavier-weight compression algorithm compared to a lighter-weightcompression algorithm that may have been originally used to compressedthe compressed data blocks. In this way, additional storage savings canbe achieved by decompressing the compressed data blocks as uncompresseddata blocks, and then recompressing the uncompressed data blocks using adifferent compression algorithm and/or compression/chunk size than whatwas originally used to compress the compressed data blocks.

In order to decompress the compressed data blocks from the originalcompression format, format information of the compressed data blocks maybe identified and used to decompress the compressed data blocks. Theformat information of the compressed data blocks may compriseinformation relating to a compression algorithm used to compress thecompressed data blocks. The format information of the compressed datablocks may comprise other metadata that may have been stored along withthe compressed data blocks, such as information relating to how todecompress the compressed data blocks. The format information comprisesenough information to decompress the compressed data blocks, along withenough information to subsequently compress the data blocks back to theoriginal compression formation, such as when the data blocks are to beread.

In another example of providing additional compression for data blocks,existing data blocks may be combined and compressed irrespective ofwhether the data blocks are compressed or not by an application and/orfile system. The resulting combined and compressed data blocks may becomprised of data blocks that were not originally compressed and/or datablocks that were originally compressed. However, additional compressionsavings may be achieved, in some embodiments, when already compresseddata blocks are first decompressed as uncompressed data blocks that arethen combined and compressed with other data blocks for additionalcompression savings.

In an embodiment of providing additional compression for existingcompressed data blocks, the node may implemented functionality throughhardware and/or software to identify if an existing data block isalready compressed or not by looking at format information stored withinthat data block (e.g., stored within a disk block of physical storagecomprising data of the data block). In response to determining that thedata block is a compressed data block based upon the format information,the compressed data block is decompressed using the format informationto create an uncompressed data block. The uncompressed data block may becombined and/or (re)compressed with other data blocks to createrecompressed data. This provides for additional compression storagesavings because the recompressed data may be compressed in a manner thatprovides more storage savings than when the recompressed data wasoriginal compressed. This can be achieved by using a heavier-weightcompression algorithm and/or a larger compression size. Thisrecompression technique may be implemented in a manner that compressesdata blocks in such a format that there is little to no negative impactupon deduplication savings (e.g., if the data was previouslydeduplicated) and/or data management functionality provided by a filesystem.

When recompressing the data blocks as the recompressed data blocks,additional information about the original format of original compressionis stored so that when the data blocks (recompressed data blocks) areread back, the data blocks can be converted back to the originalcompression format (e.g., the recompressed data blocks are decompressedand then compressed into the original compression format) beforeprocessing an operation requesting the data blocks. This additionalinformation may be stored with the recompressed data, such as at a startof a disk block of storage before the compressed data stored within thedisk block (e.g., the additional information about the originalcompression format may be stored within a disk block along with therecompressed data being stored within that disk block). In an example,compressing the data blocks back into the original compression formatmay be avoided if after decompression as uncompressed data, theuncompressed data can be directly passed to a requestor (e.g., anapplication or file accessing the data blocks) along with informationindicating that the uncompressed data has already been decompressed.

The techniques provided herein provide for the advantage of light-weightcompression (e.g., less computation and resource intensive compression,but also less storage savings) to be initially performed at anapplication or file layer. Subsequently, heavier-weight compression(e.g., more computation and resource intensive compression, with morestorage savings) may be performed at a storage layer to achieve improvedcompression savings. For example, the heavier-weight compression may beperformed at a container file level, while the light-weight compressionmay have been performed at a user file or virtual volume layer. Userfile layer compression format information may be stored within a diskblock comprising the compressed data, such as at a start of thecompressed data. This user file layer compression format information maybe used by container layer compression to decompress the data from anoriginal compression format (e.g., compression by the light-weightcompression) to obtain uncompressed data. The uncompressed data may becombined and/or compressed with other data blocks using theheavier-weight compression in order to compress a larger chunk of datathan merely the uncompressed data. The heavier-weight compression uses adifferent compression algorithm than the light-weight compressionalgorithm used by the user file layer compression in order to providethe additional storage savings.

FIG. 1 is a diagram illustrating an example operating environment 100 inwhich an embodiment of the techniques described herein may beimplemented. In one example, the techniques described herein may beimplemented within a client device 128, such as a laptop, a tablet, apersonal computer, a mobile device, a server, a virtual machine, awearable device, etc. In another example, the techniques describedherein may be implemented within one or more nodes, such as a first node130 and/or a second node 132 within a first cluster 134, a third node136 within a second cluster 138, etc. A node may comprise a storagecontroller, a server, an on-premise device, a virtual machine such as astorage virtual machine, hardware, software, or combination thereof. Theone or more nodes may be configured to manage the storage and access todata on behalf of the client device 128 and/or other client devices. Inanother example, the techniques described herein may be implementedwithin a distributed computing platform 102 such as a cloud computingenvironment (e.g., a cloud storage environment, a multi-tenant platform,a hyperscale infrastructure comprising scalable server architectures andvirtual networking, etc.) configured to manage the storage and access todata on behalf of client devices and/or nodes.

In yet another example, at least some of the techniques described hereinare implemented across one or more of the client device 128, the one ormore nodes 130, 132, and/or 136, and/or the distributed computingplatform 102. For example, the client device 128 may transmitoperations, such as data operations to read data and write data andmetadata operations (e.g., a create file operation, a rename directoryoperation, a resize operation, a set attribute operation, etc.), over anetwork 126 to the first node 130 for implementation by the first node130 upon storage. The first node 130 may store data associated with theoperations within volumes or other data objects/structures hosted withinlocally attached storage, remote storage hosted by other computingdevices accessible over the network 126, storage provided by thedistributed computing platform 102, etc. The first node 130 mayreplicate the data and/or the operations to other computing devices,such as to the second node 132, the third node 136, a storage virtualmachine executing within the distributed computing platform 102, etc.,so that one or more replicas of the data are maintained. For example,the third node 136 may host a destination storage volume that ismaintained as a replica of a source storage volume of the first node130. Such replicas can be used for disaster recovery and failover.

In an embodiment, the techniques described herein are implemented by astorage operating system or are implemented by a separate module thatinteracts with the storage operating system. The storage operatingsystem may be hosted by the client device, 128, a node, the distributedcomputing platform 102, or across a combination thereof. In an example,the storage operating system may execute within a storage virtualmachine, a hyperscaler, or other computing environment. The storageoperating system may implement a one or more file systems to logicallyorganize data within storage devices as one or more storage objects andprovide a logical/virtual representation of how the storage objects areorganized on the storage devices (e.g., a file system tailored forblock-addressable storage, a file system tailored for byte-addressablestorage such as persistent memory). A storage object may comprise anylogically definable storage element stored by the storage operatingsystem (e.g., a volume stored by the first node 130, a cloud objectstored by the distributed computing platform 102, etc.). Each storageobject may be associated with a unique identifier that uniquelyidentifies the storage object. For example, a volume may be associatedwith a volume identifier uniquely identifying that volume from othervolumes. The storage operating system also manages client access to thestorage objects.

The storage operating system may implement a file system for logicallyorganizing data. For example, the storage operating system may implementa write anywhere file layout for a volume where modified data for a filemay be written to any available location as opposed to a write-in-placearchitecture where modified data is written to the original location,thereby overwriting the previous data. In an example, the file systemmay be implemented through a file system layer that stores data of thestorage objects in an on-disk format representation that is block-based(e.g., data is stored within 4 kilobyte blocks and inodes are used toidentify files and file attributes such as creation time, accesspermissions, size and block location, etc.).

In an example, deduplication may be implemented by a deduplicationmodule associated with the storage operating system. Deduplication isperformed to improve storage efficiency. One type of deduplication isinline deduplication that ensures blocks are deduplicated before beingwritten to a storage device. Inline deduplication uses a data structure,such as an incore hash store, which maps fingerprints of data to datablocks of the storage device storing the data. Whenever data is to bewritten to the storage device, a fingerprint of that data is calculatedand the data structure is looked up using the fingerprint to findduplicates (e.g., potentially duplicate data already stored within thestorage device). If duplicate data is found, then the duplicate data isloaded from the storage device and a byte by byte comparison may beperformed to ensure that the duplicate data is an actual duplicate ofthe data to be written to the storage device. If the data to be writtenis a duplicate of the loaded duplicate data, then the data to be writtento disk is not redundantly stored to the storage device. Instead, apointer or other reference is stored in the storage device in place ofthe data to be written to the storage device. The pointer points to theduplicate data already stored in the storage device. A reference countfor the data may be incremented to indicate that the pointer nowreferences the data. If at some point the pointer no longer referencesthe data (e.g., the deduplicated data is deleted and thus no longerreferences the data in the storage device), then the reference count isdecremented. In this way, inline deduplication is able to deduplicatedata before the data is written to disk. This improves the storageefficiency of the storage device.

Background deduplication is another type of deduplication thatdeduplicates data already written to a storage device. Various types ofbackground deduplication may be implemented. In an example of backgrounddeduplication, data blocks that are duplicated between files arerearranged within storage units such that one copy of the data occupiesphysical storage. References to the single copy can be inserted into afile system structure such that all files or containers that contain thedata refer to the same instance of the data. Deduplication can beperformed on a data storage device block basis. In an example, datablocks on a storage device can be identified using a physical volumeblock number. The physical volume block number uniquely identifies aparticular block on the storage device. Additionally, blocks within afile can be identified by a file block number. The file block number isa logical block number that indicates the logical position of a blockwithin a file relative to other blocks in the file. For example, fileblock number 0 represents the first block of a file, file block number 1represents the second block, etc. File block numbers can be mapped to aphysical volume block number that is the actual data block on thestorage device. During deduplication operations, blocks in a file thatcontain the same data are deduplicated by mapping the file block numberfor the block to the same physical volume block number, and maintaininga reference count of the number of file block numbers that map to thephysical volume block number. For example, assume that file block number0 and file block number 5 of a file contain the same data, while fileblock numbers 1-4 contain unique data. File block numbers 1-4 are mappedto different physical volume block numbers. File block number 0 and fileblock number 5 may be mapped to the same physical volume block number,thereby reducing storage requirements for the file. Similarly, blocks indifferent files that contain the same data can be mapped to the samephysical volume block number. For example, if file block number 0 offile A contains the same data as file block number 3 of file B, fileblock number 0 of file A may be mapped to the same physical volume blocknumber as file block number 3 of file B.

In another example of background deduplication, a changelog is utilizedto track blocks that are written to the storage device. Backgrounddeduplication also maintains a fingerprint database (e.g., a flatmetafile) that tracks all unique block data such as by tracking afingerprint and other filesystem metadata associated with block data.Background deduplication can be periodically executed or triggered basedupon an event such as when the changelog fills beyond a threshold. Aspart of background deduplication, data in both the changelog and thefingerprint database is sorted based upon fingerprints. This ensuresthat all duplicates are sorted next to each other. The duplicates aremoved to a dup file. The unique changelog entries are moved to thefingerprint database, which will serve as duplicate data for a nextdeduplication operation. In order to optimize certain filesystemoperations needed to deduplicate a block, duplicate records in the dupfile are sorted in certain filesystem sematic order (e.g., inode numberand block number). Next, the duplicate data is loaded from the storagedevice and a whole block byte by byte comparison is performed to makesure duplicate data is an actual duplicate of the data to be written tothe storage device. After, the block in the changelog is modified topoint directly to the duplicate data as opposed to redundantly storingdata of the block.

In an example, deduplication operations performed by a datadeduplication layer of a node can be leveraged for use on another nodeduring data replication operations. For example, the first node 130 mayperform deduplication operations to provide for storage efficiency withrespect to data stored on a storage volume. The benefit of thededuplication operations performed on first node 130 can be provided tothe second node 132 with respect to the data on first node 130 that isreplicated to the second node 132. In some aspects, a data transferprotocol, referred to as the LRSE (Logical Replication for StorageEfficiency) protocol, can be used as part of replicating consistencygroup differences from the first node 130 to the second node 132. In theLRSE protocol, the second node 132 maintains a history buffer that keepstrack of data blocks that it has previously received. The history buffertracks the physical volume block numbers and file block numbersassociated with the data blocks that have been transferred from firstnode 130 to the second node 132. A request can be made of the first node130 to not transfer blocks that have already been transferred. Thus, thesecond node 132 can receive deduplicated data from the first node 130,and will not need to perform deduplication operations on thededuplicated data replicated from first node 130.

In an example, the first node 130 may preserve deduplication of datathat is transmitted from first node 130 to the distributed computingplatform 102. For example, the first node 130 may create an objectcomprising deduplicated data. The object is transmitted from the firstnode 130 to the distributed computing platform 102 for storage. In thisway, the object within the distributed computing platform 102 maintainsthe data in a deduplicated state. Furthermore, deduplication may bepreserved when deduplicated data is transmitted/replicated/mirroredbetween the client device 128, the first node 130, the distributedcomputing platform 102, and/or other nodes or devices.

In an example, compression may be implemented by a compression moduleassociated with the storage operating system. The compression module mayutilize various types of compression techniques to replace longersequences of data (e.g., frequently occurring and/or redundantsequences) with shorter sequences, such as by using Huffman coding,arithmetic coding, compression dictionaries, etc. For example, andecompressed portion of a file may comprise “ggggnnnnnnqqqqqqqqqq”,which is compressed to become “4g6n10q”. In this way, the size of thefile can be reduced to improve storage efficiency. Compression may beimplemented for compression groups. A compression group may correspondto a compressed group of blocks. The compression group may berepresented by virtual volume block numbers. The compression group maycomprise contiguous or non-contiguous blocks.

Compression may be preserved when compressed data istransmitted/replicated/mirrored between the client device 128, a node,the distributed computing platform 102, and/or other nodes or devices.For example, an object may be created by the first node 130 to comprisecompressed data. The object is transmitted from the first node 130 tothe distributed computing platform 102 for storage. In this way, theobject within the distributed computing platform 102 maintains the datain a compressed state.

In an example, various types of synchronization may be implemented by asynchronization module associated with the storage operating system. Inan example, synchronous replication may be implemented, such as betweenthe first node 130 and the second node 132. It may be appreciated thatthe synchronization module may implement synchronous replication betweenany devices within the operating environment 100, such as between thefirst node 130 of the first cluster 134 and the third node 136 of thesecond cluster 138 and/or between a node of a cluster and an instance ofa node or virtual machine in the distributed computing platform 102.

As an example, during synchronous replication, the first node 130 mayreceive a write operation from the client device 128. The writeoperation may target a file stored within a volume managed by the firstnode 130. The first node 130 replicates the write operation to create areplicated write operation. The first node 130 locally implements thewrite operation upon the file within the volume. The first node 130 alsotransmits the replicated write operation to a synchronous replicationtarget, such as the second node 132 that maintains a replica volume as areplica of the volume maintained by the first node 130. The second node132 will execute the replicated write operation upon the replica volumeso that the file within the volume and the replica volume comprises thesame data. After, the second node 132 will transmit a success message tothe first node 130. With synchronous replication, the first node 130does not respond with a success message to the client device 128 for thewrite operation until both the write operation is executed upon thevolume and the first node 130 receives the success message that thesecond node 132 executed the replicated write operation upon the replicavolume.

In another example, asynchronous replication may be implemented, such asbetween the first node 130 and the third node 136. It may be appreciatedthat the synchronization module may implement asynchronous replicationbetween any devices within the operating environment 100, such asbetween the first node 130 of the first cluster 134 and the distributedcomputing platform 102. In an example, the first node 130 may establishan asynchronous replication relationship with the third node 136. Thefirst node 130 may capture a baseline snapshot of a first volume as apoint in time representation of the first volume. The first node 130 mayutilize the baseline snapshot to perform a baseline transfer of the datawithin the first volume to the third node 136 in order to create asecond volume within the third node 136 comprising data of the firstvolume as of the point in time at which the baseline snapshot wascreated.

After the baseline transfer, the first node 130 may subsequently createsnapshots of the first volume over time. As part of asynchronousreplication, an incremental transfer is performed between the firstvolume and the second volume. In particular, a snapshot of the firstvolume is created. The snapshot is compared with a prior snapshot thatwas previously used to perform the last asynchronous transfer (e.g., thebaseline transfer or a prior incremental transfer) of data to identify adifference in data of the first volume between the snapshot and theprior snapshot (e.g., changes to the first volume since the lastasynchronous transfer). Accordingly, the difference in data isincrementally transferred from the first volume to the second volume. Inthis way, the second volume will comprise the same data as the firstvolume as of the point in time when the snapshot was created forperforming the incremental transfer. It may be appreciated that othertypes of replication may be implemented, such as semi-sync replication.

In an embodiment, the first node 130 may store data or a portion thereofwithin storage hosted by the distributed computing platform 102 bytransmitting the data within objects to the distributed computingplatform 102. In one example, the first node 130 may locally storefrequently accessed data within locally attached storage. Lessfrequently accessed data may be transmitted to the distributed computingplatform 102 for storage within a data storage tier 108. The datastorage tier 108 may store data within a service data store 120, and maystore client specific data within client data stores assigned to suchclients such as a client (1) data store 122 used to store data of aclient (1) and a client (N) data store 124 used to store data of aclient (N). The data stores may be physical storage devices or may bedefined as logical storage, such as a virtual volume, LUNs, or otherlogical organizations of data that can be defined across one or morephysical storage devices. In another example, the first node 130transmits and stores all client data to the distributed computingplatform 102. In yet another example, the client device 128 transmitsand stores the data directly to the distributed computing platform 102without the use of the first node 130.

The management of storage and access to data can be performed by one ormore storage virtual machines (SVMs) or other storage applications thatprovide software as a service (SaaS) such as storage software services.In one example, an SVM may be hosted within the client device 128,within the first node 130, or within the distributed computing platform102 such as by the application server tier 106. In another example, oneor more SVMs may be hosted across one or more of the client device 128,the first node 130, and the distributed computing platform 102. The oneor more SVMs may host instances of the storage operating system.

In an example, the storage operating system may be implemented for thedistributed computing platform 102. The storage operating system mayallow client devices to access data stored within the distributedcomputing platform 102 using various types of protocols, such as aNetwork File System (NFS) protocol, a Server Message Block (SMB)protocol and Common Internet File System (CIFS), and Internet SmallComputer Systems Interface (iSCSI), and/or other protocols. The storageoperating system may provide various storage services, such as disasterrecovery (e.g., the ability to non-disruptively transition clientdevices from accessing a primary node that has failed to a secondarynode that is taking over for the failed primary node), backup andarchive function, replication such as asynchronous and/or synchronousreplication, deduplication, compression, high availability storage,cloning functionality (e.g., the ability to clone a volume, such as aspace efficient flex clone), snapshot functionality (e.g., the abilityto create snapshots and restore data from snapshots), data tiering(e.g., migrating infrequently accessed data to slower/cheaper storage),encryption, managing storage across various platforms such as betweenon-premise storage systems and multiple cloud systems, etc.

In one example of the distributed computing platform 102, one or moreSVMs may be hosted by the application server tier 106. For example, aserver (1) 116 is configured to host SVMs used to execute applicationssuch as storage applications that manage the storage of data of theclient (1) within the client (1) data store 122. Thus, an SVM executingon the server (1) 116 may receive data and/or operations from the clientdevice 128 and/or the first node 130 over the network 126. The SVMexecutes a storage application and/or an instance of the storageoperating system to process the operations and/or store the data withinthe client (1) data store 122. The SVM may transmit a response back tothe client device 128 and/or the first node 130 over the network 126,such as a success message or an error message. In this way, theapplication server tier 106 may host SVMs, services, and/or otherstorage applications using the server (1) 116, the server (N) 118, etc.

A user interface tier 104 of the distributed computing platform 102 mayprovide the client device 128 and/or the first node 130 with access touser interfaces associated with the storage and access of data and/orother services provided by the distributed computing platform 102. In anexample, a service user interface 110 may be accessible from thedistributed computing platform 102 for accessing services subscribed toby clients and/or nodes, such as data replication services, applicationhosting services, data security services, human resource services,warehouse tracking services, accounting services, etc. For example,client user interfaces may be provided to corresponding clients, such asa client (1) user interface 112, a client (N) user interface 114, etc.The client (1) can access various services and resources subscribed toby the client (1) through the client (1) user interface 112, such asaccess to a web service, a development environment, a human resourceapplication, a warehouse tracking application, and/or other services andresources provided by the application server tier 106, which may usedata stored within the data storage tier 108.

The client device 128 and/or the first node 130 may subscribe to certaintypes and amounts of services and resources provided by the distributedcomputing platform 102. For example, the client device 128 may establisha subscription to have access to three virtual machines, a certainamount of storage, a certain type/amount of data redundancy, a certaintype/amount of data security, certain service level agreements (SLAs)and service level objectives (SLOs), latency guarantees, bandwidthguarantees, access to execute or host certain applications, etc.Similarly, the first node 130 can establish a subscription to haveaccess to certain services and resources of the distributed computingplatform 102.

As shown, a variety of clients, such as the client device 128 and thefirst node 130, incorporating and/or incorporated into a variety ofcomputing devices may communicate with the distributed computingplatform 102 through one or more networks, such as the network 126. Forexample, a client may incorporate and/or be incorporated into a clientapplication (e.g., software) implemented at least in part by one or moreof the computing devices.

Examples of suitable computing devices include personal computers,server computers, desktop computers, nodes, storage servers, nodes,laptop computers, notebook computers, tablet computers or personaldigital assistants (PDAs), smart phones, cell phones, and consumerelectronic devices incorporating one or more computing devicecomponents, such as one or more electronic processors, microprocessors,central processing units (CPU), or controllers. Examples of suitablenetworks include networks utilizing wired and/or wireless communicationtechnologies and networks operating in accordance with any suitablenetworking and/or communication protocol (e.g., the Internet). In usecases involving the delivery of customer support services, the computingdevices noted represent the endpoint of the customer support deliveryprocess, i.e., the consumer's device.

The distributed computing platform 102, such as a multi-tenant businessdata processing platform or cloud computing environment, may includemultiple processing tiers, including the user interface tier 104, theapplication server tier 106, and a data storage tier 108. The userinterface tier 104 may maintain multiple user interfaces, includinggraphical user interfaces and/or web-based interfaces. The userinterfaces may include the service user interface 110 for a service toprovide access to applications and data for a client (e.g., a “tenant”)of the service, as well as one or more user interfaces that have beenspecialized/customized in accordance with user specific requirements(e.g., as discussed above), which may be accessed via one or more APIs.

The service user interface 110 may include components enabling a tenantto administer the tenant's participation in the functions andcapabilities provided by the distributed computing platform 102, such asaccessing data, causing execution of specific data processingoperations, etc. Each processing tier may be implemented with a set ofcomputers, virtualized computing environments such as a storage virtualmachine or storage virtual server, and/or computer components includingcomputer servers and processors, and may perform various functions,methods, processes, or operations as determined by the execution of asoftware application or set of instructions.

The data storage tier 108 may include one or more data stores, which mayinclude the service data store 120 and one or more client data stores122-124. Each client data store may contain tenant-specific data that isused as part of providing a range of tenant-specific business andstorage services or functions, including but not limited to ERP, CRM,eCommerce, Human Resources management, payroll, storage services, etc.Data stores may be implemented with any suitable data storagetechnology, including structured query language (SQL) based relationaldatabase management systems (RDBMS), file systems hosted by operatingsystems, object storage, etc.

The distributed computing platform 102 may be a multi-tenant and serviceplatform operated by an entity in order to provide multiple tenants witha set of business related applications, data storage, and functionality.These applications and functionality may include ones that a businessuses to manage various aspects of its operations. For example, theapplications and functionality may include providing web-based access tobusiness information systems, thereby allowing a user with a browser andan Internet or intranet connection to view, enter, process, or modifycertain types of business information or any other type of information.

A clustered network environment 200 that may implement one or moreaspects of the techniques described and illustrated herein is shown inFIG. 2. The clustered network environment 200 includes data storageapparatuses 202(1)-202(n) that are coupled over a cluster or clusterfabric 204 that includes one or more communication network(s) andfacilitates communication between the data storage apparatuses202(1)-202(n) (and one or more modules, components, etc. therein, suchas, node computing devices 206(1)-206(n), for example), although anynumber of other elements or components can also be included in theclustered network environment 200 in other examples. This technologyprovides a number of advantages including methods, non-transitorycomputer readable media, and computing devices that implement thetechniques described herein.

In this example, node computing devices 206(1)-206(n) can be primary orlocal storage controllers or secondary or remote storage controllersthat provide client devices 208(1)-208(n) with access to data storedwithin data storage devices 210(1)-210(n) and cloud storage device(s)236 (also referred to as cloud storage node(s)). The node computingdevices 206(1)-206(n) may be implemented as hardware, software (e.g., astorage virtual machine), or combination thereof.

The data storage apparatuses 202(1)-202(n) and/or node computing devices206(1)-206(n) of the examples described and illustrated herein are notlimited to any particular geographic areas and can be clustered locallyand/or remotely via a cloud network, or not clustered in other examples.Thus, in one example the data storage apparatuses 202(1)-202(n) and/ornode computing device 206(1)-206(n) can be distributed over a pluralityof storage systems located in a plurality of geographic locations (e.g.,located on-premise, located within a cloud computing environment, etc.);while in another example a clustered network can include data storageapparatuses 202(1)-202(n) and/or node computing device 206(1)-206(n)residing in a same geographic location (e.g., in a single on-site rack).

In the illustrated example, one or more of the client devices208(1)-208(n), which may be, for example, personal computers (PCs),computing devices used for storage (e.g., storage servers), or othercomputers or peripheral devices, are coupled to the respective datastorage apparatuses 202(1)-202(n) by network connections 212(1)-212(n).Network connections 212(1)-212(n) may include a local area network (LAN)or wide area network (WAN) (i.e., a cloud network), for example, thatutilize TCP/IP and/or one or more Network Attached Storage (NAS)protocols, such as a Common Internet Filesystem (CIFS) protocol or aNetwork Filesystem (NFS) protocol to exchange data packets, a StorageArea Network (SAN) protocol, such as Small Computer System Interface(SCSI) or Fiber Channel Protocol (FCP), an object protocol, such assimple storage service (S3), and/or non-volatile memory express (NVMe),for example.

Illustratively, the client devices 208(1)-208(n) may be general-purposecomputers running applications and may interact with the data storageapparatuses 202(1)-202(n) using a client/server model for exchange ofinformation. That is, the client devices 208(1)-208(n) may request datafrom the data storage apparatuses 202(1)-202(n) (e.g., data on one ofthe data storage devices 210(1)-210(n) managed by a network storagecontroller configured to process I/O commands issued by the clientdevices 208(1)-208(n)), and the data storage apparatuses 202(1)-202(n)may return results of the request to the client devices 208(1)-208(n)via the network connections 212(1)-212(n).

The node computing devices 206(1)-206(n) of the data storage apparatuses202(1)-202(n) can include network or host nodes that are interconnectedas a cluster to provide data storage and management services, such as toan enterprise having remote locations, cloud storage (e.g., a storageendpoint may be stored within cloud storage device(s) 236), etc., forexample. Such node computing devices 206(1)-206(n) can be attached tothe cluster fabric 204 at a connection point, redistribution point, orcommunication endpoint, for example. One or more of the node computingdevices 206(1)-206(n) may be capable of sending, receiving, and/orforwarding information over a network communications channel, and couldcomprise any type of device that meets any or all of these criteria.

In an example, the node computing devices 206(1) and 206(n) may beconfigured according to a disaster recovery configuration whereby asurviving node provides switchover access to the storage devices210(1)-210(n) in the event a disaster occurs at a disaster storage site(e.g., the node computing device 206(1) provides client device 212(n)with switchover data access to data storage devices 210(n) in the eventa disaster occurs at the second storage site). In other examples, thenode computing device 206(n) can be configured according to an archivalconfiguration and/or the node computing devices 206(1)-206(n) can beconfigured based on another type of replication arrangement (e.g., tofacilitate load sharing). Additionally, while two node computing devicesare illustrated in FIG. 2, any number of node computing devices or datastorage apparatuses can be included in other examples in other types ofconfigurations or arrangements.

As illustrated in the clustered network environment 200, node computingdevices 206(1)-206(n) can include various functional components thatcoordinate to provide a distributed storage architecture. For example,the node computing devices 206(1)-206(n) can include network modules214(1)-214(n) and disk modules 216(1)-216(n). Network modules214(1)-214(n) can be configured to allow the node computing devices206(1)-206(n) (e.g., network storage controllers) to connect with clientdevices 208(1)-208(n) over the storage network connections212(1)-212(n), for example, allowing the client devices 208(1)-208(n) toaccess data stored in the clustered network environment 200.

Further, the network modules 214(1)-214(n) can provide connections withone or more other components through the cluster fabric 204. Forexample, the network module 214(1) of node computing device 206(1) canaccess the data storage device 210(n) by sending a request via thecluster fabric 204 through the disk module 216(n) of node computingdevice 206(n) when the node computing device 206(n) is available.Alternatively, when the node computing device 206(n) fails, the networkmodule 214(1) of node computing device 206(1) can access the datastorage device 210(n) directly via the cluster fabric 204. The clusterfabric 204 can include one or more local and/or wide area computingnetworks (i.e., cloud networks) embodied as Infiniband, Fibre Channel(FC), or Ethernet networks, for example, although other types ofnetworks supporting other protocols can also be used.

Disk modules 216(1)-216(n) can be configured to connect data storagedevices 210(1)-210(n), such as disks or arrays of disks, SSDs, flashmemory, or some other form of data storage, to the node computingdevices 206(1)-206(n). Often, disk modules 216(1)-216(n) communicatewith the data storage devices 210(1)-210(n) according to the SANprotocol, such as SCSI or FCP, for example, although other protocols canalso be used. Thus, as seen from an operating system on node computingdevices 206(1)-206(n), the data storage devices 210(1)-210(n) can appearas locally attached. In this manner, different node computing devices206(1)-206(n), etc. may access data blocks, files, or objects throughthe operating system, rather than expressly requesting abstract files.

While the clustered network environment 200 illustrates an equal numberof network modules 214(1)-214(n) and disk modules 216(1)-216(n), otherexamples may include a differing number of these modules. For example,there may be a plurality of network and disk modules interconnected in acluster that do not have a one-to-one correspondence between the networkand disk modules. That is, different node computing devices can have adifferent number of network and disk modules, and the same nodecomputing device can have a different number of network modules thandisk modules.

Further, one or more of the client devices 208(1)-208(n) can benetworked with the node computing devices 206(1)-206(n) in the cluster,over the storage connections 212(1)-212(n). As an example, respectiveclient devices 208(1)-208(n) that are networked to a cluster may requestservices (e.g., exchanging of information in the form of data packets)of node computing devices 206(1)-206(n) in the cluster, and the nodecomputing devices 206(1)-206(n) can return results of the requestedservices to the client devices 208(1)-208(n). In one example, the clientdevices 208(1)-208(n) can exchange information with the network modules214(1)-214(n) residing in the node computing devices 206(1)-206(n)(e.g., network hosts) in the data storage apparatuses 202(1)-202(n).

In one example, the storage apparatuses 202(1)-202(n) host aggregatescorresponding to physical local and remote data storage devices, such aslocal flash or disk storage in the data storage devices 210(1)-210(n),for example. One or more of the data storage devices 210(1)-210(n) caninclude mass storage devices, such as disks of a disk array. The disksmay comprise any type of mass storage devices, including but not limitedto magnetic disk drives, flash memory, and any other similar mediaadapted to store information, including, for example, data and/or parityinformation.

The aggregates include volumes 218(1)-218(n) in this example, althoughany number of volumes can be included in the aggregates. The volumes218(1)-218(n) are virtual data stores or storage objects that define anarrangement of storage and one or more filesystems within the clusterednetwork environment 200. Volumes 218(1)-218(n) can span a portion of adisk or other storage device, a collection of disks, or portions ofdisks, for example, and typically define an overall logical arrangementof data storage. In one example volumes 218(1)-218(n) can include storeduser data as one or more files, blocks, or objects that may reside in ahierarchical directory structure within the volumes 218(1)-218(n).

Volumes 218(1)-218(n) are typically configured in formats that may beassociated with particular storage systems, and respective volumeformats typically comprise features that provide functionality to thevolumes 218(1)-218(n), such as providing the ability for volumes218(1)-218(n) to form clusters, among other functionality. Optionally,one or more of the volumes 218(1)-218(n) can be in composite aggregatesand can extend between one or more of the data storage devices210(1)-210(n) and one or more of the cloud storage device(s) 236 toprovide tiered storage, for example, and other arrangements can also beused in other examples.

In one example, to facilitate access to data stored on the disks orother structures of the data storage devices 210(1)-210(n), a filesystemmay be implemented that logically organizes the information as ahierarchical structure of directories and files. In this example,respective files may be implemented as a set of disk blocks of aparticular size that are configured to store information, whereasdirectories may be implemented as specially formatted files in whichinformation about other files and directories are stored.

Data can be stored as files or objects within a physical volume and/or avirtual volume, which can be associated with respective volumeidentifiers. The physical volumes correspond to at least a portion ofphysical storage devices, such as the data storage devices 210(1)-210(n)(e.g., a Redundant Array of Independent (or Inexpensive) Disks (RAIDsystem)) whose address, addressable space, location, etc. does notchange. Typically the location of the physical volumes does not changein that the range of addresses used to access it generally remainsconstant.

Virtual volumes, in contrast, can be stored over an aggregate ofdisparate portions of different physical storage devices. Virtualvolumes may be a collection of different available portions of differentphysical storage device locations, such as some available space fromdisks, for example. It will be appreciated that since the virtualvolumes are not “tied” to any one particular storage device, virtualvolumes can be said to include a layer of abstraction or virtualization,which allows it to be resized and/or flexible in some regards.

Further, virtual volumes can include one or more logical unit numbers(LUNs), directories, Qtrees, files, and/or other storage objects, forexample. Among other things, these features, but more particularly theLUNs, allow the disparate memory locations within which data is storedto be identified, for example, and grouped as data storage unit. Assuch, the LUNs may be characterized as constituting a virtual disk ordrive upon which data within the virtual volumes is stored within anaggregate. For example, LUNs are often referred to as virtual drives,such that they emulate a hard drive, while they actually comprise datablocks stored in various parts of a volume.

In one example, the data storage devices 210(1)-210(n) can have one ormore physical ports, wherein each physical port can be assigned a targetaddress (e.g., SCSI target address). To represent respective volumes, atarget address on the data storage devices 210(1)-210(n) can be used toidentify one or more of the LUNs. Thus, for example, when one of thenode computing devices 206(1)-206(n) connects to a volume, a connectionbetween the one of the node computing devices 206(1)-206(n) and one ormore of the LUNs underlying the volume is created.

Respective target addresses can identify multiple of the LUNs, such thata target address can represent multiple volumes. The I/O interface,which can be implemented as circuitry and/or software in a storageadapter or as executable code residing in memory and executed by aprocessor, for example, can connect to volumes by using one or moreaddresses that identify the one or more of the LUNs.

Referring to FIG. 3, node computing device 206(1) in this particularexample includes processor(s) 300, a memory 302, a network adapter 304,a cluster access adapter 306, and a storage adapter 308 interconnectedby a system bus 310. In other examples, the node computing device 206(1)comprises a virtual machine, such as a virtual storage machine. The nodecomputing device 206(1) also includes a storage operating system 312installed in the memory 302 that can, for example, implement a RAID dataloss protection and recovery scheme to optimize reconstruction of dataof a failed disk or drive in an array, along with other functionalitysuch as deduplication, compression, snapshot creation, data mirroring,synchronous replication, asynchronous replication, encryption, etc. Insome examples, the node computing device 206(n) is substantially thesame in structure and/or operation as node computing device 206(1),although the node computing device 206(n) can also include a differentstructure and/or operation in one or more aspects than the nodecomputing device 206(1). In an example, a file system may be implementedfor persistent memory.

The network adapter 304 in this example includes the mechanical,electrical and signaling circuitry needed to connect the node computingdevice 206(1) to one or more of the client devices 208(1)-208(n) overnetwork connections 212(1)-212(n), which may comprise, among otherthings, a point-to-point connection or a shared medium, such as a localarea network. In some examples, the network adapter 304 furthercommunicates (e.g., using TCP/IP) via the cluster fabric 204 and/oranother network (e.g. a WAN) (not shown) with cloud storage device(s)236 to process storage operations associated with data stored thereon.

The storage adapter 308 cooperates with the storage operating system 312executing on the node computing device 206(1) to access informationrequested by one of the client devices 208(1)-208(n) (e.g., to accessdata on a data storage device 210(1)-210(n) managed by a network storagecontroller). The information may be stored on any type of attached arrayof writeable media such as magnetic disk drives, flash memory, and/orany other similar media adapted to store information.

In the exemplary data storage devices 210(1)-210(n), information can bestored in data blocks on disks. The storage adapter 308 can include I/Ointerface circuitry that couples to the disks over an I/O interconnectarrangement, such as a storage area network (SAN) protocol (e.g., SmallComputer System Interface (SCSI), Internet SCSI (iSCSI), hyperSCSI,Fiber Channel Protocol (FCP)). The information is retrieved by thestorage adapter 308 and, if necessary, processed by the processor(s) 300(or the storage adapter 308 itself) prior to being forwarded over thesystem bus 310 to the network adapter 304 (and/or the cluster accessadapter 306 if sending to another node computing device in the cluster)where the information is formatted into a data packet and returned to arequesting one of the client devices 208(1)-208(n) and/or sent toanother node computing device attached via the cluster fabric 204. Insome examples, a storage driver 314 in the memory 302 interfaces withthe storage adapter to facilitate interactions with the data storagedevices 210(1)-210(n).

The storage operating system 312 can also manage communications for thenode computing device 206(1) among other devices that may be in aclustered network, such as attached to a cluster fabric 204. Thus, thenode computing device 206(1) can respond to client device requests tomanage data on one of the data storage devices 210(1)-210(n) or cloudstorage device(s) 236 (e.g., or additional clustered devices) inaccordance with the client device requests.

The file system module 318 of the storage operating system 312 canestablish and manage one or more filesystems including software code anddata structures that implement a persistent hierarchical namespace offiles and directories, for example. As an example, when a new datastorage device (not shown) is added to a clustered network system, thefile system module 318 is informed where, in an existing directory tree,new files associated with the new data storage device are to be stored.This is often referred to as “mounting” a filesystem.

In the example node computing device 206(1), memory 302 can includestorage locations that are addressable by the processor(s) 300 andadapters 304, 306, and 308 for storing related software application codeand data structures. The processor(s) 300 and adapters 304, 306, and 308may, for example, include processing elements and/or logic circuitryconfigured to execute the software code and manipulate the datastructures.

In the example, the node computing device 206(1) comprises persistentmemory 320. The persistent memory 320 comprises a plurality of pageswithin which data can be stored. The plurality of pages may be indexedby page block numbers.

The storage operating system 312, portions of which are typicallyresident in the memory 302 and executed by the processor(s) 300, invokesstorage operations in support of a file service implemented by the nodecomputing device 206(1). Other processing and memory mechanisms,including various computer readable media, may be used for storingand/or executing application instructions pertaining to the techniquesdescribed and illustrated herein. For example, the storage operatingsystem 312 can also utilize one or more control files (not shown) to aidin the provisioning of virtual machines.

In this particular example, the memory 302 also includes a moduleconfigured to implement the techniques described herein, as discussedabove and further below.

The examples of the technology described and illustrated herein may beembodied as one or more non-transitory computer or machine readablemedia, such as the memory 302, having machine or processor-executableinstructions stored thereon for one or more aspects of the presenttechnology, which when executed by processor(s), such as processor(s)300, cause the processor(s) to carry out the steps necessary toimplement the methods of this technology, as described and illustratedwith the examples herein. In some examples, the executable instructionsare configured to perform one or more steps of a method described andillustrated later.

One embodiment of implementing additional compression for existingcompressed data is illustrated by an exemplary method 400 of FIG. 4,which is further described in conjunction with system 500 of FIGS.5A-5D. A node 502 may store data on behalf of client devices withinstorage 514, such as solid state drives, disk drives, memory, cloudstorage, or a variety of other types of storage or combinations thereof,as illustrated by FIG. 5A. The data may be stored within disk blocks ofphysical storage, and may be organized by a file system for access bythe client devices. For example, the node 502 may store data on behalfof an application 508. In an example, the application 508 may be hostedon a remote client device that connects to the node 502 and/or thestorage 514 over a network. In another example, the application 508 maybe hosted on the node 502 that is connected to the storage 514. The node502 may be directly connected to the storage 514 or may be connected tothe storage 514 over a network.

In an example, the node 502 may maintain data (A) within a block (A) 516within the storage 514. The node 502 may maintain data (B) within ablock (B) 518 within the storage 514. The node 5002 may maintain data(C) within a block (C) 520 within the storage. The block (A) 516, theblock (B) 518, the block (C) 520, and/or other blocks within the storage514 may correspond to disk blocks of physical storage within one or morestorage devices. In an embodiment, the data (A), the data (B), and/orthe data (C) may have been compressed by the application 508 before thedata (A), the data (B), and/or the data (C) was written to correspondingfiles within the storage 514. For example, an application/file layer 510may have utilized user file layer compression 512 to compress the data(A), the data (B), and/or the data (C) before the data (A), the data(B), and/or the data (C) was written to the storage 514. The user filelayer compression 512 may comprise a relatively lighter-weightcompression that consumes relatively less resources, processing power,and time to compress compared to other heavier-weight compression thatcan provide more compression storage savings than the lighter-weightcompression, but consumes more resources, processing power, and/or time.

In another embodiment, the data (A), the data (B), and/or the data (C)may have been compressed at a file level (e.g., corresponding to theapplication/file layer 510) by combining and compressing some fileblocks comprising the data (A), the data (B), and/or the data (C). Inthis way, the block (A) 516 is a compressed block (A) 516 comprisingcompressed data (A), the block (B) 518 is a compressed block (B) 518comprising compressed data (B), and the block (C) 520 is a compressedblock (C) 520 comprising compressed data (C). In an example, thecompressed data (A), the compressed data (B), and/or the compressed data(C) may have been compressed at a user file layer, virtual volume layer,etc. (e.g., by the application/file layer 510).

In an embodiment, the compressed data (A), the compressed data (B), thecompressed data (C), and/or other compressed data within the storage 514may have been compressed using a first compression algorithm, such asthe lighter-weight compression algorithm. The first compressionalgorithm may compress the data (A) to create the compressed data (A)(e.g., compress the block (A) 516 to create the compressed block (A)516), the data (B) to create the compressed data (B) 518 (e.g., compressthe block (B) 518 to create the compressed block (B) 518), and the data(C) to create the compressed data (C) (e.g., compress the block (C) 520to create the compressed block (C) 520) according to a first compressionformat and/or first compression size utilized by the first compressionalgorithm. The layer, such as the application/file layer 510, thatexecutes the first compression algorithm may store format informationwithin the compressed block (A) 516, the compressed block (B) 518, andthe compressed block (C) 520. That is, the layer may store formatinformation for a compressed data block within an actual physical diskblock comprising compressed data of the compressed data block, such aswithin a start of the physical disk block before the compressed datawithin the physical disk block.

In an example, format information (A) is stored within the compressedblock (A) 516. The format information (A) may identify the firstcompression algorithm used to compress the compressed block (A) 516. Theformat information (A) may comprise information relating to how todecompress the compressed block (A) 516. The format information (A) maycomprise information relating to how to compress the data (A) to obtainthe compressed data (A) such as the compressed block (A) 516. Formatinformation (B) is stored within the compressed block (B) 518. Theformat information (B) may identify the first compression algorithm usedto compress the compressed block (B) 518. The format information (B) maycomprise information relating to how to decompress the compressed block(B) 518. The format information (B) may comprise information relating tohow to compress the data (B) to obtain the compressed data (B) such asthe compressed block (B) 518. Format information (C) is stored withinthe compressed block (C) 520. The format information (C) may identifythe first compression algorithm used to compress the compressed block(C) 520. The format information (C) may comprise information relating tohow to decompress the compressed block (C) 520. The format information(C) may comprise information relating to how to compress the data (C) toobtain the compressed data (C) such as the compressed block (C) 520.

The node 502 may be configured with a container file layer 504 that canperform operations upon data within the storage 514 at a container level(e.g., a volume level or any other container level associated with acontainer within which files may be stored/contained). The containerfile layer 504 may be capable of performing container level compression506. The container level compression 506 may compress data at acontainer level as opposed to at a user file or virtual volume levelsuch as the user file layer compression 512. The container levelcompression 506 may provide a relatively heavier-compression than thelighter-weight compression provided by the user file layer compression512. For example, the container level compression 506 may utilize alarger compression size (e.g., a larger chunk size) in order to provideadditional compression storage savings than the user file layercompression 512 initially used to create the compressed block (A) 516,the compressed block (B) 518, and the compressed block (C) 520. However,the container level compression 506 may utilize more resources and/ortime, but provides for improved storage efficiency.

The application 508, hosted on a client device, on the node 502, orother location, may write data directly to the storage 514 or write thedata through the node 502 to the storage 514 (e.g., a write operationmay be transmitted from the application 508 to the node 502, and thenode 502 may execute the write operation upon the storage 514). Forexample, the application 508 may determine that data (D) is to bewritten to a file located within the storage 514, as illustrated by FIG.5B. The application 508 may utilize the application/file layer 510 toimplement the user file layer compression 512 upon the data (D) tocreate compressed data (D) that is compressed into a first compressionformat using a first compression algorithm. In an embodiment, theapplication 508 may store the compressed data (D) within the storage514. In another embodiment, the application 508 may transmit thecompressed data (D) to the node 502, and the node 502 may store 530 thecompressed data (D) within the storage 514. The compressed data (D) maybe stored within the storage 514 as a compressed block (D) 532comprising the compressed data (D) and format information indicatingthat the first compression algorithm was used to compress the compresseddata (D) along with other information indicating how to decompress thecompressed data (D).

In order to achieve additional storage compression efficiency forreducing storage consumption within the storage 514, the existingcompressed block (A) 516, the existing compressed block (B) 518, theexisting compressed block (C) 520, the existing compressed block (D)532, and/or other compressed data within the storage 514 that wascompressed with the first compression algorithm into the firstcompression format (e.g., compressed by the application/file layer 510using the user file layer compression 512 that implements thelighter-weight compression algorithm) are additionally compressed usinga second compression algorithm. The second compression algorithm maycorrespond to the heavier-weight compression algorithm and/or largercompression size implemented by the container file layer 504 using thecontainer level compression 506.

Accordingly, at 402, the node 502 may evaluate the compressed block (D)532 to determine whether additional compression can be performed for thecompressed block (D) 532, as illustrated by FIG. 5C. In particular, thenode 502 may evaluate the format information stored within thecompressed block (D) 532 to determine whether the compressed block (D)532 is compressed or uncompressed. The format information may indicatethat the compressed block (D) 532 is compressed. The format informationmay indicate that the first compression algorithm was used to compressthe compressed block (D) 532. In an example, the compressed block (D)532 may be determined, based upon the format information, to have beencompressed by the application 508 using the first compression format. Inan example, the compressed block (D) 532 may be determined, based uponthe format information, to have been compressed at a file layer (e.g.,the application/file layer 510 of the application 508) using the firstcompression format corresponding to the user file layer compression 512(e.g., a user file layer compression format). The format information maycomprise information indicating how to decompress the compressed block(D) 532. In an embodiment, the format information may be stored within adisk block (e.g., a physical disk block within the storage 514)comprising the compressed block (D) 532, such as within a start of thedisk block before the compressed data (D) of the compressed block (D)532.

At 404, the compressed block (D) 532 may be decompressed by the node 502in response to the determination that the compressed block (D) 532 iscompressed according to the first compression format by the firstcompression algorithm. The compressed block (D) 532 may be decompressedusing the format information. For example, the compressed block (D) 532may be compressed using information within the format information thatindicates how to decompress the compressed block (D) 532. In this way,the compressed block (D) 532 is decompressed to obtain data (D) of block(D) in an uncompressed state.

At 406, the node 502 compresses (recompression 540) the data (D) of theblock (D) with one or more other data blocks within the storage 514 tocreate compressed data (recompressed data 542) having a secondcompression format different than the first compression format of thecompressed block (D) 532. In an example, the block (D) and the one ormore other data blocks may be compressed together using a secondcompression algorithm different than the first compression algorithmused to compress the compressed block (D) 532. For example, therelatively lighter-weight compression algorithm may have been used bythe application/file layer 510 of the application 508 to perform theuser file layer compression to create the compressed block (D) 532. Incontrast, the container file layer 504 of the node 502 may implement thecontainer level compression 506 to compress the block (D) and the one ormore other data blocks within the storage 514 according to a relativelyheavier-weight compression algorithm to create the recompressed data 542in the second compression format. In an embodiment, the block (D) andthe one or more other data blocks within the storage 514 may becompressed using the second compression format at a storage layer of thenode 502 to create the recompressed data 542 in the second compressionformat.

In an embodiment, the second compression format of the secondcompression algorithm used to create the recompressed data 542, whichmay utilize a second compression size that is different than a firstcompression size that was utilized by the first compression algorithm ofthe first compression format of the compressed block (D) 532. Forexample, the second compression size may be larger than the firstcompression size (e.g., a larger chunk of data may be compressed usingthe second compression size such as where not only the block (D) isbeing compressed, but the block (D) is being compressed with the one ormore additional data blocks within the storage 514).

In an embodiment, the recompressed data 542 may retain any priordeduplication performed upon the data (D) (e.g., deduplication performedupon the compressed block (D) 532) and/or the additional data blocksthat are recompressed with the data (D). For example, data within datablocks and compressed data blocks (e.g., data of the compressed block(A) 516, the compressed block (B) 518, the compressed block (C) 520, andthe compressed block (D) 532) in the storage 514 may have beendeduplicated to identify and remove redundant data by replacingduplicate instances of the same data with references to a singleinstance of the data in order to reduce storage utilization otherwisewasted in storing redundant data within the storage 514. When thecompressed block (D) 532 is decompressed and recompressed as therecompressed data 542, any prior deduplication with respect to thecompressed block (D) 532 may be retained within the recompressed data542.

In an embodiment, the container file layer 504 of the node 502 mayutilize the container level compression to retain compressioninformation within the recompressed data 542. The compressioninformation may correspond to and/or be derived from the formatinformation that was included within the compressed block (D) 532. Forexample, the compression information may comprise information regardingthe first compression format of the compressed block (D) 532. In anexample, the compression information may comprise information about thefirst compression algorithm. In an example, the compression informationmay comprise information about the first compression size. In anexample, the compression information may comprise information used tocompress the data (D) back into the first compression format as thecompressed block (D) 532. In an embodiment, the compression informationmay be stored within a disk block of the storage 514 at a start locationof the disk block at a location occurring before the recompressed data542 (e.g., a physical disk block of the storage 514 may comprise thecompression information, and then comprise the recompressed data 542).

FIG. 5D illustrates a requestor 552 transmitting a request 554 to thenode 502 for the data (D). In an example, the requestor 552 may comprisethe application 508, a client device, or other entity requesting 554 thedata (D) (e.g., a device attempting to read the data (D) of a fileaccessed by the device or to write to the data (D) of the file). Uponreceiving the request 554 for the data (D), the node 502 accesses 556the recompressed data 542 within the storage 514. In an embodiment, thenode 502 may decompress the recompressed data 542 using the compressioninformation in order to obtain the data (D) (the block (D)). The data(D) may be transmitted from the node 502 to the requestor 552 in anuncompressed format, along with an indication that the data (D) is nolonger compressed. In another embodiment, the node may decompress therecompressed data 542 using the compression information in order toobtain the data (D) (the block (D)). The node 502 may utilize thecompression information to recompress the data (D) (the block (D)) intothe first compression format in order to obtain the compressed block (D)532. The node 502 may transmit the compressed block (D) 532 to therequestor 552.

Still another embodiment involves a computer-readable medium 600comprising processor-executable instructions configured to implement oneor more of the techniques presented herein. An example embodiment of acomputer-readable medium or a computer-readable device that is devisedin these ways is illustrated in FIG. 6, wherein the implementationcomprises a computer-readable medium 608, such as a compactdisc-recordable (CD-R), a digital versatile disc-recordable (DVD-R),flash drive, a platter of a hard disk drive, etc., on which is encodedcomputer-readable data 606. This computer-readable data 606, such asbinary data comprising at least one of a zero or a one, in turncomprises processor-executable computer instructions 604 configured tooperate according to one or more of the principles set forth herein. Insome embodiments, the processor-executable computer instructions 604 areconfigured to perform a method 602, such as at least some of theexemplary method 400 of FIG. 4, for example. In some embodiments, theprocessor-executable computer instructions 604 are configured toimplement a system, such as at least some of the exemplary system 500 ofFIGS. 5A-5D, for example. Many such computer-readable media arecontemplated to operate in accordance with the techniques presentedherein.

In an embodiment, the described methods and/or their equivalents may beimplemented with computer executable instructions. Thus, in anembodiment, a non-transitory computer readable/storage medium isconfigured with stored computer executable instructions of analgorithm/executable application that when executed by a machine(s)cause the machine(s) (and/or associated components) to perform themethod. Example machines include but are not limited to a processor, acomputer, a server operating in a cloud computing system, a serverconfigured in a Software as a Service (SaaS) architecture, a smartphone, and so on. In an embodiment, a computing device is implementedwith one or more executable algorithms that are configured to performany of the disclosed methods.

It will be appreciated that processes, architectures and/or proceduresdescribed herein can be implemented in hardware, firmware and/orsoftware. It will also be appreciated that the provisions set forthherein may apply to any type of special-purpose computer (e.g., filehost, storage server and/or storage serving appliance) and/orgeneral-purpose computer, including a standalone computer or portionthereof, embodied as or including a storage system. Moreover, theteachings herein can be configured to a variety of storage systemarchitectures including, but not limited to, a network-attached storageenvironment and/or a storage area network and disk assembly directlyattached to a client or host computer. Storage system should thereforebe taken broadly to include such arrangements in addition to anysubsystems configured to perform a storage function and associated withother equipment or systems.

In some embodiments, methods described and/or illustrated in thisdisclosure may be realized in whole or in part on computer-readablemedia. Computer readable media can include processor-executableinstructions configured to implement one or more of the methodspresented herein, and may include any mechanism for storing this datathat can be thereafter read by a computer system. Examples of computerreadable media include (hard) drives (e.g., accessible via networkattached storage (NAS)), Storage Area Networks (SAN), volatile andnon-volatile memory, such as read-only memory (ROM), random-accessmemory (RAM), electrically erasable programmable read-only memory(EEPROM) and/or flash memory, compact disk read only memory (CD-ROM)s,CD-Rs, compact disk re-writeable (CD-RW)s, DVDs, cassettes, magnetictape, magnetic disk storage, optical or non-optical data storage devicesand/or any other medium which can be used to store data.

Although the subject matter has been described in language specific tostructural features or methodological acts, it is to be understood thatthe subject matter defined in the appended claims is not necessarilylimited to the specific features or acts described above. Rather, thespecific features and acts described above are disclosed as exampleforms of implementing at least some of the claims.

Various operations of embodiments are provided herein. The order inwhich some or all of the operations are described should not beconstrued to imply that these operations are necessarily orderdependent. Alternative ordering will be appreciated given the benefit ofthis description. Further, it will be understood that not all operationsare necessarily present in each embodiment provided herein. Also, itwill be understood that not all operations are necessary in someembodiments.

Furthermore, the claimed subject matter is implemented as a method,apparatus, or article of manufacture using standard application orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer application accessible from anycomputer-readable device, carrier, or media. Of course, manymodifications may be made to this configuration without departing fromthe scope or spirit of the claimed subject matter.

As used in this application, the terms “component”, “module,” “system”,“interface”, and the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentincludes a process running on a processor, a processor, an object, anexecutable, a thread of execution, an application, or a computer. By wayof illustration, both an application running on a controller and thecontroller can be a component. One or more components residing within aprocess or thread of execution and a component may be localized on onecomputer or distributed between two or more computers.

Moreover, “exemplary” is used herein to mean serving as an example,instance, illustration, etc., and not necessarily as advantageous. Asused in this application, “or” is intended to mean an inclusive “or”rather than an exclusive “or”. In addition, “a” and “an” as used in thisapplication are generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Also, at least one of A and B and/or the like generally means A orB and/or both A and B. Furthermore, to the extent that “includes”,“having”, “has”, “with”, or variants thereof are used, such terms areintended to be inclusive in a manner similar to the term “comprising”.

Many modifications may be made to the instant disclosure withoutdeparting from the scope or spirit of the claimed subject matter. Unlessspecified otherwise, “first,” “second,” or the like are not intended toimply a temporal aspect, a spatial aspect, an ordering, etc. Rather,such terms are merely used as identifiers, names, etc. for features,elements, items, etc. For example, a first set of information and asecond set of information generally correspond to set of information Aand set of information B or two different or two identical sets ofinformation or the same set of information.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure. In addition, while aparticular feature of the disclosure may have been disclosed withrespect to only one of several implementations, such feature may becombined with one or more other features of the other implementations asmay be desired and advantageous for any given or particular application.

What is claimed is:
 1. A method, comprising: evaluating formatinformation stored within a data block to determine whether the datablock is compressed or uncompressed; and in response to the data blockbeing compressed according to a first compression format: decompressingthe data block using the format information; and compressing the datablock with one or more other data blocks to create compressed datahaving a second compression format different than the first compressionformat.
 2. The method of claim 1, comprising: evaluating the formatinformation to identify a compression algorithm used to compress thedata block.
 3. The method of claim 1, comprising: evaluating the formatinformation to identify information indicating how to decompress thedata block.
 4. The method of claim 1, wherein compressing the data blockcomprises: retaining deduplication of the data block.
 5. The method ofclaim 1, comprising: retaining compression information regarding thefirst compression format of the data block, wherein the compressioninformation is derived from the format information.
 6. The method ofclaim 5, comprising: in response to receiving a request to read the datablock, converting the data block into the first compression format usingthe compression information.
 7. The method of claim 5, wherein thecompression information is stored within a start of the compressed datastored in a disk block.
 8. The method of claim 1, comprising: inresponse to receiving a request to read the data block, decompressingthe compressed data to obtain the data block in an uncompressed state.9. A non-transitory machine readable medium comprising instructions forperforming a method, which when executed by a machine, causes themachine to: determine that a data block has been compressed using afirst compression format; decompress the data block using formatinformation indicating a compression algorithm used to compress the datablock and information indicating how to decompress the data block; andcompress the data block with one or more other data blocks to createcompressed data having a second compression format different than thefirst compression format.
 10. The non-transitory machine readable mediumof claim 9, wherein the data block is determined to have been compressedby an application using the first compression format.
 11. Thenon-transitory machine readable medium of claim 9, wherein the datablock is determined to have been compressed at a file layer using thefirst compression format corresponding to a user file layer compressionformat.
 12. The non-transitory machine readable medium of claim 9,wherein the data block is compressed using the second compression formatat a container file layer.
 13. The non-transitory machine readablemedium of claim 9, wherein the data block is compressed using the secondcompression format at a storage layer.
 14. The non-transitory machinereadable medium of claim 9, wherein the first compression formatcorresponds to a first compression size and the second compressionformat corresponds to a second compression size different than the firstcompression size.
 15. The non-transitory machine readable medium ofclaim 14, wherein the first compression size is smaller than the secondcompression size.
 16. A computing device comprising: a memory comprisingmachine executable code for performing a method; and a processor coupledto the memory, the processor configured to execute the machineexecutable code to cause the processor to: determine that a data blockhas been compressed using a first compression algorithm; decompress thedata block using format information corresponding to the firstcompression algorithm; and compress the data block with one or moreother data blocks using a second compression algorithm to createcompressed data.
 17. The computing device of claim 16, wherein the firstcompression algorithm is different than the second compressionalgorithm.
 18. The computing device of claim 16, wherein the firstcompression algorithm utilizes a first compression size and the secondcompression algorithm utilizes a second compression size.
 19. Thecomputing device of claim 18, wherein the first compression size issmaller than the second compression size.
 20. The computing device ofclaim 16, wherein the machine executable code causes the processor to:in response to receiving a request to read the data block, decompressthe compressed data to obtain the data block in an uncompressed state.